The ‘Asabiyya-Driven Structuration of Women’s Breast Cancer in the Arab Region
Bibliographic record
Abstract
The Arab region witnesses more than 60% of reported breast cancer cases detected at a late stage, stage III or higher. Establishing a well-founded regional network ensures maximum impact in regards to the implementation and success of preventative and early detection measures. This paper explores and evaluates the ‘asabiyya-driven structuration—the cohesive force of the group that gives it strength in facing its struggles for progressive reproduction—of influential agents for breast cancer prevention and early detection in the Arab region. The layers of the philosophical standing from Ibn Khaldûn’s concept of ‘asabiyya and the theoretical foundation of social systems theory, structuration theory, social network analysis, and social capital theory are peeled in order to explore and evaluate the context, constraints, social networks, autopoiesis, and social capital. Utilizing a qualitative research design, this study employs content analysis and in-depth interviews as data collection methods and NVivo as an analysis tool. Data is collected from 122 publications and knowledgeable informants employed by cancer agents, ministries of health, and World Health Organization offices in Egypt, Jordan, Morocco, and Oman. Findings reveal that countries with a national cancer control program witness local strengthening ‘asabiyya and ‘asabiyya-driven structuration, while those without a national cancer control program witness weakening local ‘asabiyya. Thus, strategic recommendations are proposed to accelerate the regional ‘asabiyya-driven structuration for preventative and early detection measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".